Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A system for compensating for user hand tremors when using a hand-held electronic device having a user interface display, comprising: a processor and a memory configured to provide computer program instructions to the processor to execute the function of the components: a position monitoring component for monitoring position data of a user's finger in relation to the user interface display over time as the finger approaches an element in the user interface display; a target determining component for determining a target element by predicting an intended movement of the user's finger; an element enlarging component for dynamically enlarging the determined target element in the user interface display as the user's finger approaches the user interface display; wherein the target determining component includes, an averaging component for averaging position data of the finger in the (x,y) plane of the user interface display as the finger approaches the user interface display; and a confidence component for applying a confidence coefficient to weight the averaging, wherein a confidence coefficient weights the confidence of position data at different distances in direction (z) perpendicular to the (x,y) plane to provide a compensation method to accommodate different tremor types.
The system addresses the problem of user hand tremors interfering with precise interactions on hand-held electronic devices, such as smartphones or tablets, by dynamically adjusting the user interface to compensate for unintended movements. The system includes a processor and memory that execute components to monitor the user's finger position relative to the display as it approaches a target element. A position monitoring component tracks the finger's movement in the (x,y) plane of the display over time. A target determining component predicts the intended target element by averaging the finger's position data, with a confidence component applying a confidence coefficient to weight the averaging based on the finger's distance (z-axis) from the display. This accommodates different tremor types by adjusting the influence of position data at varying distances. An element enlarging component dynamically enlarges the predicted target element as the finger approaches, improving accuracy for users with tremors. The system enhances usability by reducing the impact of involuntary hand movements on touchscreen interactions.
2. The system as claimed in claim 1 , including: a tremor analyzing component for analyzing the position data of the user's finger to determine a type of tremor in the finger; and a compensation type component for selecting a compensation method for the type of tremor and using the compensation method in determining the intended element.
This invention relates to a system for improving user interaction with touch-sensitive devices by compensating for finger tremors. The problem addressed is the difficulty users with tremors face when interacting with touchscreens, where unintended inputs occur due to involuntary finger movements. The system analyzes the user's finger position data to identify the specific type of tremor affecting the user, such as essential tremor or Parkinson's tremor, and then selects an appropriate compensation method to correct the input. The compensation method adjusts the detected finger position to determine the intended target element on the screen, reducing errors and improving usability. The system may also include a calibration component to establish baseline finger movement patterns and a tremor classification component to categorize the tremor type based on frequency, amplitude, and other characteristics. By dynamically adapting to the user's tremor patterns, the system enhances accuracy in touch interactions for individuals with motor impairments.
3. The system as claimed in claim 1 , wherein the target determining component includes an updating component for dynamically updating the determining of a target element as more position data becomes available as the finger approaches the user interface display, and wherein the element enlarging component dynamically enlarges a new determined target element.
A system for improving touch target selection on a user interface display addresses the challenge of accurately selecting small or closely spaced interactive elements when using a finger. The system includes a target determining component that identifies a potential target element based on initial position data from the finger's approach. As the finger moves closer to the display, an updating component dynamically refines the target selection by incorporating additional position data, ensuring the most relevant element is selected even if the finger's path changes. Simultaneously, an element enlarging component dynamically increases the size of the newly determined target element, making it easier to accurately touch. This adaptive approach enhances usability by reducing selection errors and improving precision, particularly in interfaces with dense or small interactive elements. The system continuously adjusts both the target selection and visual feedback as the finger approaches, ensuring optimal interaction accuracy.
4. The system as claimed in claim 2 , wherein the compensation type component includes: an intention tremor compensation component for an intention tremor in which the movement of the finger becomes more erratic as the finger approaches the user interface display, and the compensation method applies a greater confidence to the accuracy of earlier monitored positions of the user's finger; a Parkinsonian tremor compensation component for a Parkinsonian tremor in which the movement of the finger becomes less erratic as the finger approaches the user interface display, and the compensation method applies a lesser confidence to the accuracy of earlier monitored positions of the user's finger; and an essential tremor compensation component for an essential tremor in which the movement of the finger is constantly erratic as the finger approaches the user interface display, and the compensation method applies an equal confidence to the accuracy of earlier monitored positions of the user's finger.
A system for compensating for different types of tremors in user interface interactions involves a compensation type component that distinguishes between intention tremors, Parkinsonian tremors, and essential tremors. For intention tremors, where finger movement becomes more erratic as it approaches a display, the system applies greater confidence to earlier monitored finger positions to stabilize input. For Parkinsonian tremors, where movement becomes less erratic near the display, the system applies lesser confidence to earlier positions, prioritizing more recent data. For essential tremors, characterized by constant erratic movement, the system applies equal confidence to all monitored positions. The compensation methods dynamically adjust input interpretation based on the detected tremor type to improve accuracy in touch or gesture-based interfaces. This approach enhances usability for individuals with neurological conditions affecting motor control, ensuring smoother and more reliable interaction with digital displays.
5. The system as claimed in claim 1 , including: a stabilizing component for sensing a movement of the hand-held electronic device and stabilizing the user interface display relative to the device to compensate for the movement.
A system for stabilizing the display of a hand-held electronic device addresses the problem of unintended movement causing visual instability in the user interface. The device includes a stabilizing component that detects movement of the hand-held device and adjusts the displayed user interface to compensate for that movement. This ensures that the user interface remains stable and readable despite physical motion of the device. The stabilizing component may use sensors such as accelerometers, gyroscopes, or other motion detection mechanisms to track movement in multiple axes. The system processes sensor data in real-time to calculate necessary adjustments and applies transformations to the display output, such as panning, tilting, or scaling, to counteract the detected motion. This compensation can be applied to the entire display or specific elements within the interface. The stabilization may also include adaptive algorithms that adjust the level of compensation based on the type or intensity of movement. The goal is to provide a smoother, more stable viewing experience for the user, particularly in scenarios where the device is used in motion, such as during walking, running, or other activities where hand tremors or vibrations occur. The system may further incorporate user preferences or environmental factors to optimize stabilization performance.
6. The system as claimed in claim 1 , wherein the target determining component includes a context analysis component for applying context analysis to aid in prediction of a next target element.
A system for target determination in a computational or interactive environment analyzes contextual data to predict the next target element. The system includes a context analysis component that processes contextual information, such as user behavior, environmental factors, or system state, to enhance the accuracy of target predictions. By applying context analysis, the system improves the selection or recommendation of subsequent elements, such as actions, objects, or data points, based on dynamic conditions. This approach optimizes decision-making processes in applications like user interfaces, automation systems, or predictive modeling, where adaptive targeting is critical. The context analysis component may integrate machine learning, statistical modeling, or rule-based logic to refine predictions, ensuring relevance and efficiency in real-time or batch processing scenarios. The system's ability to adapt predictions based on contextual insights reduces errors and enhances performance in target-driven applications.
7. The system as claimed in claim 1 , wherein the target determining component includes an eye tracking component for tracking a movement of the user's eye and using the tracking in determining a target element.
This invention relates to a system for determining a target element in a user interface based on eye tracking. The system addresses the problem of accurately identifying a user's intended target in interactive environments, such as virtual reality, augmented reality, or computer interfaces, where traditional input methods like touch or mouse may be impractical or imprecise. The system includes an eye tracking component that monitors the movement of the user's eye to determine where the user is looking. This eye tracking data is then used to identify a target element, such as an icon, button, or other interactive object, within the user's field of view. The system may also include additional components, such as a gaze prediction module that anticipates the user's intended target based on historical or contextual data, and a selection confirmation module that verifies the user's intent before executing an action. The eye tracking component may use infrared cameras, gaze estimation algorithms, or other techniques to track eye movement with high precision. By leveraging eye tracking, the system enables more intuitive and efficient interaction with digital interfaces, particularly in scenarios where hands-free operation is desired. The invention improves upon prior art by reducing input latency and increasing accuracy in target selection, making it suitable for applications in gaming, medical devices, and accessibility tools.
8. The system as claimed in claim 1 , wherein the determining a target element by the predicting of the intended movement of the user's finger includes applying context analysis to aid in a prediction of a next target element.
A system for predicting user interaction with a touch-sensitive interface applies context analysis to improve the accuracy of determining a target element based on the intended movement of a user's finger. The system monitors finger movement patterns and environmental or application-specific context to anticipate the next target element the user intends to select. Context analysis may include factors such as the user's historical interaction patterns, the layout of the interface, the type of application being used, or external conditions like ambient lighting or device orientation. By incorporating these contextual clues, the system enhances prediction accuracy, reducing errors in target selection and improving responsiveness. This approach is particularly useful in touch-based interfaces where precise finger positioning is challenging, such as on large displays or in dynamic environments. The system dynamically adjusts predictions based on real-time context, ensuring smoother and more intuitive user interactions.
9. A computer program product for compensating for user hand tremors when using a hand-held electronic device having a user interface display, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to: monitor position data of a user's finger in relation to the user interface display over time as the finger approaches an element in the user interface display; determine a target element by predicting an intended movement of the user's finger; dynamically enlarge the determined target element in the user interface display as the user's finger approaches the user interface display; average position data of the finger in the (x,y) plane of the user interface display as the finger approaches the user interface display; and apply a confidence coefficient to weight the averaging, wherein a confidence coefficient weights the confidence of position data at different distances in direction (z) perpendicular to the (x,y) plane to provide a compensation method to accommodate different tremor types.
The invention relates to a computer program product designed to compensate for user hand tremors when interacting with a hand-held electronic device, such as a smartphone or tablet. Hand tremors can make it difficult for users to accurately select elements on a touchscreen interface, leading to frustration and reduced usability. The program monitors the position of a user's finger in relation to the device's display as the finger approaches a user interface element. It predicts the intended movement of the finger to determine the target element and dynamically enlarges this element as the finger gets closer to the display. The program also averages the finger's position data in the (x,y) plane of the display to smooth out tremors. Additionally, it applies a confidence coefficient to weight the averaging process, adjusting the influence of position data based on the finger's distance (z-axis) from the display. This allows the system to adapt to different types of tremors, improving accuracy and usability for users with varying degrees of hand instability. The solution enhances touchscreen interaction by dynamically adjusting the target area and compensating for involuntary movements.
10. The system as claimed in claim 8 , wherein the applying context analysis applies n-gram analysis to predict a next letter or word.
The invention relates to a system for enhancing text input or prediction in computing devices, particularly for improving accuracy and efficiency in text entry. The system addresses the challenge of predicting the next letter or word in a sequence, which is critical for applications like autocomplete, predictive text, and virtual keyboards. The system applies context analysis to improve prediction accuracy, and specifically uses n-gram analysis to model the likelihood of the next letter or word based on preceding text. N-gram analysis involves statistical modeling of sequences of n items (letters, words, or other units) to identify patterns and probabilities in language usage. By leveraging n-gram analysis, the system can dynamically adapt to the user's input, reducing errors and speeding up text entry. The system may also incorporate additional context analysis techniques, such as semantic or syntactic analysis, to further refine predictions. The overall goal is to provide a more intuitive and efficient text input experience by accurately anticipating the user's intended input.
Unknown
August 18, 2020
Browse 5M+ US patents with plain-English claim translations and AI-generated analysis.